The Establishment of a Sheep Embryo Genomic Selection System
Bibliographic record
Abstract
Embryo genomic selection (EGS) is a contemporary breeding strategy that combines genomic selection (GS) methodology with embryo biotechnology. By conducting genotyping and genomic prediction at the pre-implantation stage, embryos with superior breeding value can be identified for transfer, markedly increasing breeding efficiency while reducing the uncertainty and temporal expenditure associated with conventional GS. This study establishes a reliable embryo biopsy-based GS pipeline for sheep, incorporating optimized whole-genome amplification and microcell genotyping techniques. We developed a high-efficiency in vitro sheep embryo production platform compatible with embryo biopsy. Systematic comparison of Multiple Displacement Amplification (MDA) and Multiple Annealing and Looping Based Amplification Cycles (MALBAC) whole-genome amplification systems yielded high-quality genotypes from biopsy samples of embryos containing as few as 10 cells. Imputation using 10× whole-genome sequencing data significantly increased both genotype call rates and accuracy. High concordance was observed between embryo and lamb genotypes, and genomic estimated breeding values (GEBVs) for key growth traits exhibited strong correlations (R2: 0.91–0.98). This system enables accurate preimplantation genomic evaluation and provides an efficient strategy to accelerate genetic improvement in sheep breeding programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".